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The course offers an introduction to data analysis in the transdisciplinary field of empirical (medical) research in the programming language R. The R system for statistical computing is openly available at https://www.r-project.org and provides a simple and flexible software environment for statistical analyses and graphics. Tailored to the application in empirical research, the course covers the basics of programming and data formats in R, as well as the essential steps of a data analysis including data manipulation, descriptive statistics, statistical tests and graphical representations. Reflections on research methodology and transdisciplinarity are addressed and critical thinking is encouraged.
The Get R_eady module enhances digital skills in the context of data analysis and graphics. It stimulates critical thinking and provides tools for reproducible research. In applications corresponding to the course participants' backgrounds, the concepts are explained. Hands-on sessions facilitate the understanding of the participants and enhance the discussion.
The course is building on problem based learning and held in an interactive and diverse format, with short lectures, demonstrations and hands on practical exercises to be solved in small groups.
MA, PhD
Larger collections of data are becoming increasingly available. To exploit their potential, statistical analysis skills are needed. The direct link between data and visualization/reporting of results is highly relevant in all empirical research disciplines, as several scientific fields have recently been criticized for lack of reproducibility.
Dynamic reporting tools can be used to directly link data, visualization and analysis outputs, allowing for rapid adaption after possible changes in the dataset, e.g. after data preparation, validation or in the context of manuscript revision.
Tailored to applications in empirical research, the course covers the basics of dynamic report compilation in RMarkdown, including examples of dynamic reports for presentations, manuscripts, and html websites. Research methodology is reflected upon, especially in relation to reproducibility, Open Science and transdisciplinarity. Exemplary reports from different disciplines will be compiled and presented by the students.
MA, PhD
1 ECTS
You can find more information about the module here.
Prognostic models to predict future events have increasingly been used across different fields, e.g. in the medical sciences (clinical prediction models, personalized medicine, prognostic models), in legal data science (predictive analytics), political sciences (scientific prediction), or related.
The derivation and validation of such models poses specific challenges, that require knowledge of distinct methodological aspects in order to develop models that are internally valid and can be generalized out-of-sample. This course covers traditional statistical as well as machine learning approaches for model development, sample size calculation, variable selection, methodological outcomes for the assessment of model performance, as well as model validation. The course encourages critical thinking regarding published prognostic models’ validity across different fields of research.
Aims of the course:
MA, PhD
1 ECTS
You can find more information about the module here.
